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Genome feature exploration using hyperbolic Self-Organising Maps
Martin, Christian ; Diaz, Naryttza N. ; Ontrup, Jörg ; Nattkemper, Tim W.
The advent of sequencing technologies allows to reassess the relationship between species in the hierarchically organized tree of life. Self-Organizing Maps (SOM) in Euclidean and hyperbolic space are applied to genomic signatures of 350 different organisms of the two superkingdoms Bacteria and Archaea to link the sequence signature space to pre-defined taxonomic levels, i.e. the tree of life. In the hyperbolic space the SOMs are trained by either the standard algorithm (HSOM) or in a hierarchical manner (H²SOM). For evaluating the SOM performances, distances between organisms in the feature space, on the SOM grid and in the taxonomy tree are compared pair-wise. We show that the structure recovered using the different SOMs reflects the gold standard of current taxonomy. The distances between species are better preserved when using the HSOM or H²SOM which makes the hyperbolic space better suited for embedding the high dimensional genomic signatures.
||self organization, hyperbolic SOM, whole genome analysis, information visualization
||Faculty of Technology, Research Groups in Informatics
||Data processing, computer science, computer systems
Martin, Christian ; Diaz, Naryttza N. ; Ontrup, Jörg ; Nattkemper, Tim W. (2007) Genome feature exploration using hyperbolic Self-Organising Maps.